Search

All-solid-state batteries using Si as the anode have shown promising performance without continual solid-electrolyte interface (SEI) growth. However, the first cycle irreversible capacity loss yields low initial Coulombic efficiency (ICE) of Si, limiting the energy density. To address this, we adopt a prelithiation strategy to increase ICE and conductivity of all-solid-state Si cells. A significant increase in ICE is observed for Li1Si anode paired with a lithium cobalt oxide (LCO) cathode. Additionally, a comparison with lithium nickel manganese cobalt oxide (NCM) reveals that performance improvements with Si prelithiation is only applicable for full cells dominated by high anode irreversibility. With this prelithiation strategy, 15% improvement in capacity retention is achieved after 1000 cycles compared to a pure Si. With Li1Si, a high areal capacity of up to 10 mAh cm-2 is attained using a dry-processed LCO cathode film, suggesting that the prelithiation method may be suitable for high-loading next-generation all-solid-state batteries.

Objective: The significant impact of fatigue on the lives of patients with chronic conditions has demanded a response. One response has been the development and testing of self-management programs. Little is known about what these programs have in common or how they differ. This scoping review compared the key components of fatigue self-management programs. Methods: Scoping review methodology was employed. Databases of CINAHL, Academic Search Premier, PsycINFO, Cochrane and Medline were searched to identify relevant sources. Results: Included fatigue programs were compared using a three-component framework: 1) self-management strategies; 2) active patient participation; and 3) self-management support. Although all programs included some aspects of these components, the extent varied with only a few domains of these components found across all programs. Conclusion: The three self-management components employed in this study showed potential benefits in identifying similarities and differences across fatigue programs with comparable and distinct underlying theories. This three-component framework could facilitate identification of domains associated with positive outcomes. Practice implications: It is essential that authors of programs provide detailed descriptions to enable inter-program comparison. The three-component framework chosen for this review was capable of describing and comparing fatigue self-management programs, paving the way for more effective interventions.

Objective: The significant impact of fatigue on the lives of patients with chronic conditions has demanded a response. One response has been the development and testing of self-management programs. Little is known about what these programs have in common or how they differ. This scoping review compared the key components of fatigue self-management programs. Methods: Scoping review methodology was employed. Databases of CINAHL, Academic Search Premier, PsycINFO, Cochrane and Medline were searched to identify relevant sources. Results: Included fatigue programs were compared using a three-component framework: 1) self-management strategies; 2) active patient participation; and 3) self-management support. Although all programs included some aspects of these components, the extent varied with only a few domains of these components found across all programs. Conclusion: The three self-management components employed in this study showed potential benefits in identifying similarities and differences across fatigue programs with comparable and distinct underlying theories. This three-component framework could facilitate identification of domains associated with positive outcomes. Practice implications: It is essential that authors of programs provide detailed descriptions to enable inter-program comparison. The three-component framework chosen for this review was capable of describing and comparing fatigue self-management programs, paving the way for more effective interventions.

Ye Zhou,1,* Ming-Jun Liu,2,* Xiu-Yu Liao,1 Yu-Ting Chen,3 Qiu-Xia Liao,1 Jian-Dong Lin,1 Hai-Rong Lin,4 Ying-Hong Huang1 1Department of Intensive Care Unit, First Affiliated Hospital of Fujian Medical University, Fuzhou, 350000, People’s Republic of China; 2Department of Infection, People’s Hospital of YangJiang, YangJiang, 529500, People’s Republic of China; 3Department of Gastroenterology, Fuzhou NO. 1 Hospital, Fuzhou, 350000, People’s Republic of China; 4Department of Intensive Care Unit, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiu-Yu Liao; Ying-Hong Huang, Department of Intensive Care Unit, First Affiliated Hospital of Fujian Medical University, No. 20 of Chazhong Road, TaiJiang District, Fuzhou, 350000, People’s Republic of China, Tel +86 15959021313 ; +86 13306901596, Fax +86 591-87982028, Email liaoxiuyullxuiyu@126.com; huangyinghonghyh@126.comObjective: To investigate the antibacterial impact of daptomycin and azithromycin in vitro on methicillin-resistant Staphylococcus aureus (MRSA) biofilm.Methods: (1) Measure the strain growth curve and the biofilm formation curve. (2) Determine the minimum inhibitory concentrations (MICs) of daptomycin and azithromycin. (3) Investigate the antibacterial impact of the combination of daptomycin and azithromycin. (4) Perform the evaluation of the intervention impact of antimicrobial agents on MRSA biofilm. (5) Observe the biofilm after intervention with the antibacterial agent.Results: (1) MRSA exhibited three phases: lag phase (0– 4 h), logarithmic growth (4– 8 h) and stationary phase after 18 h; its biofilm began to form at 6 h, semi-matured at 24 h, and reached maturity after 48 h. (2) The MICs of daptomycin and azithromycin were 8 μg/mL and greater than 256 μg/mL, respectively. (3) The combination of dapto

Objective: The significant impact of fatigue on the lives of patients with chronic conditions has demanded a response. One response has been the development and testing of self-management programs. Little is known about what these programs have in common or how they differ. This scoping review compared the key components of fatigue self-management programs. Methods: Scoping review methodology was employed. Databases of CINAHL, Academic Search Premier, PsycINFO, Cochrane and Medline were searched to identify relevant sources. Results: Included fatigue programs were compared using a three-component framework: 1) self-management strategies; 2) active patient participation; and 3) self-management support. Although all programs included some aspects of these components, the extent varied with only a few domains of these components found across all programs. Conclusion: The three self-management components employed in this study showed potential benefits in identifying similarities and differences across fatigue programs with comparable and distinct underlying theories. This three-component framework could facilitate identification of domains associated with positive outcomes. Practice implications: It is essential that authors of programs provide detailed descriptions to enable inter-program comparison. The three-component framework chosen for this review was capable of describing and comparing fatigue self-management programs, paving the way for more effective interventions.

Objective: The significant impact of fatigue on the lives of patients with chronic conditions has demanded a response. One response has been the development and testing of self-management programs. Little is known about what these programs have in common or how they differ. This scoping review compared the key components of fatigue self-management programs. Methods: Scoping review methodology was employed. Databases of CINAHL, Academic Search Premier, PsycINFO, Cochrane and Medline were searched to identify relevant sources. Results: Included fatigue programs were compared using a three-component framework: 1) self-management strategies; 2) active patient participation; and 3) self-management support. Although all programs included some aspects of these components, the extent varied with only a few domains of these components found across all programs. Conclusion: The three self-management components employed in this study showed potential benefits in identifying similarities and differences across fatigue programs with comparable and distinct underlying theories. This three-component framework could facilitate identification of domains associated with positive outcomes. Practice implications: It is essential that authors of programs provide detailed descriptions to enable inter-program comparison. The three-component framework chosen for this review was capable of describing and comparing fatigue self-management programs, paving the way for more effective interventions.

Objective: The significant impact of fatigue on the lives of patients with chronic conditions has demanded a response. One response has been the development and testing of self-management programs. Little is known about what these programs have in common or how they differ. This scoping review compared the key components of fatigue self-management programs. Methods: Scoping review methodology was employed. Databases of CINAHL, Academic Search Premier, PsycINFO, Cochrane and Medline were searched to identify relevant sources. Results: Included fatigue programs were compared using a three-component framework: 1) self-management strategies; 2) active patient participation; and 3) self-management support. Although all programs included some aspects of these components, the extent varied with only a few domains of these components found across all programs. Conclusion: The three self-management components employed in this study showed potential benefits in identifying similarities and differences across fatigue programs with comparable and distinct underlying theories. This three-component framework could facilitate identification of domains associated with positive outcomes. Practice implications: It is essential that authors of programs provide detailed descriptions to enable inter-program comparison. The three-component framework chosen for this review was capable of describing and comparing fatigue self-management programs, paving the way for more effective interventions.

Purpose: To select and scale items for the seven domains of the Patient-Reported Inventory of Self-Management of Chronic Conditions (PRISM-CC) and assess its construct validity. Methods: Using an online survey, data on 100 potential items, and other variables for assessing construct validity, were collected from 1055 adults with one or more chronic health conditions. Based on a validated conceptual model, confirmatory factor analysis (CFA) and item response models (IRT) were used to select and scale potential items and assess the internal consistency and structural validity of the PRISM-CC. To further assess construct validity, hypothesis testing of known relationships was conducted using structural equation models. Results: Of 100 potential items, 36 (4–8 per domain) were selected, providing excellent fit to our hypothesized correlated factors model and demonstrating internal consistency and structural validity of the PRISM-CC. Hypothesized associations between PRISM-CC domains and other measures and variables were confirmed, providing further evidence of construct validity. Conclusion: The PRISM-CC overcomes limitations of assessment tools currently available to measure patient self-management of chronic health conditions. This study provides strong evidence for the internal consistency and construct validity of the PRISM-CC as an instrument to assess patient-reported difficulty in self-managing different aspects of daily life with one or more chronic conditions. Further research is needed to assess its measurement equivalence across patient attributes, ability to measure clinically important change, and utility to inform self-management support.

Purpose: To select and scale items for the seven domains of the Patient-Reported Inventory of Self-Management of Chronic Conditions (PRISM-CC) and assess its construct validity. Methods: Using an online survey, data on 100 potential items, and other variables for assessing construct validity, were collected from 1055 adults with one or more chronic health conditions. Based on a validated conceptual model, confirmatory factor analysis (CFA) and item response models (IRT) were used to select and scale potential items and assess the internal consistency and structural validity of the PRISM-CC. To further assess construct validity, hypothesis testing of known relationships was conducted using structural equation models. Results: Of 100 potential items, 36 (4–8 per domain) were selected, providing excellent fit to our hypothesized correlated factors model and demonstrating internal consistency and structural validity of the PRISM-CC. Hypothesized associations between PRISM-CC domains and other measures and variables were confirmed, providing further evidence of construct validity. Conclusion: The PRISM-CC overcomes limitations of assessment tools currently available to measure patient self-management of chronic health conditions. This study provides strong evidence for the internal consistency and construct validity of the PRISM-CC as an instrument to assess patient-reported difficulty in self-managing different aspects of daily life with one or more chronic conditions. Further research is needed to assess its measurement equivalence across patient attributes, ability to measure clinically important change, and utility to inform self-management support.

Purpose: To select and scale items for the seven domains of the Patient-Reported Inventory of Self-Management of Chronic Conditions (PRISM-CC) and assess its construct validity. Methods: Using an online survey, data on 100 potential items, and other variables for assessing construct validity, were collected from 1055 adults with one or more chronic health conditions. Based on a validated conceptual model, confirmatory factor analysis (CFA) and item response models (IRT) were used to select and scale potential items and assess the internal consistency and structural validity of the PRISM-CC. To further assess construct validity, hypothesis testing of known relationships was conducted using structural equation models. Results: Of 100 potential items, 36 (4–8 per domain) were selected, providing excellent fit to our hypothesized correlated factors model and demonstrating internal consistency and structural validity of the PRISM-CC. Hypothesized associations between PRISM-CC domains and other measures and variables were confirmed, providing further evidence of construct validity. Conclusion: The PRISM-CC overcomes limitations of assessment tools currently available to measure patient self-management of chronic health conditions. This study provides strong evidence for the internal consistency and construct validity of the PRISM-CC as an instrument to assess patient-reported difficulty in self-managing different aspects of daily life with one or more chronic conditions. Further research is needed to assess its measurement equivalence across patient attributes, ability to measure clinically important change, and utility to inform self-management support.

All-solid-state batteries have recently gained considerable attention due to their potential improvements in safety, energy density, and cycle-life compared to conventional liquid electrolyte batteries. Sodium all-solid-state batteries also offer the potential to eliminate costly materials containing lithium, nickel, and cobalt, making them ideal for emerging grid energy storage applications. However, significant work is required to understand the persisting limitations and long-term cyclability of Na all-solid-state-based batteries. In this work, we demonstrate the importance of careful solid electrolyte selection for use against an alloy anode in Na all-solid-state batteries. Three emerging solid electrolyte material classes were chosen for this study: the chloride Na2.25Y0.25Zr0.75Cl6, sulfide Na3PS4, and borohydride Na2(B10H10)0.5(B12H12)0.5. Focused ion beam scanning electron microscopy (FIB-SEM) imaging, X-ray photoelectron spectroscopy (XPS), and electrochemical impedance spectroscopy (EIS) were utilized to characterize the evolution of the anode-electrolyte interface upon electrochemical cycling. The obtained results revealed that the interface stability is determined by both the intrinsic electrochemical stability of the solid electrolyte and the passivating properties of the formed interfacial products. With appropriate material selection for stability at the respective anode and cathode interfaces, stable cycling performance can be achieved for Na all-solid-state batteries.

Purpose: To select and scale items for the seven domains of the Patient-Reported Inventory of Self-Management of Chronic Conditions (PRISM-CC) and assess its construct validity. Methods: Using an online survey, data on 100 potential items, and other variables for assessing construct validity, were collected from 1055 adults with one or more chronic health conditions. Based on a validated conceptual model, confirmatory factor analysis (CFA) and item response models (IRT) were used to select and scale potential items and assess the internal consistency and structural validity of the PRISM-CC. To further assess construct validity, hypothesis testing of known relationships was conducted using structural equation models. Results: Of 100 potential items, 36 (4–8 per domain) were selected, providing excellent fit to our hypothesized correlated factors model and demonstrating internal consistency and structural validity of the PRISM-CC. Hypothesized associations between PRISM-CC domains and other measures and variables were confirmed, providing further evidence of construct validity. Conclusion: The PRISM-CC overcomes limitations of assessment tools currently available to measure patient self-management of chronic health conditions. This study provides strong evidence for the internal consistency and construct validity of the PRISM-CC as an instrument to assess patient-reported difficulty in self-managing different aspects of daily life with one or more chronic conditions. Further research is needed to assess its measurement equivalence across patient attributes, ability to measure clinically important change, and utility to inform self-management support.

Purpose: To select and scale items for the seven domains of the Patient-Reported Inventory of Self-Management of Chronic Conditions (PRISM-CC) and assess its construct validity. Methods: Using an online survey, data on 100 potential items, and other variables for assessing construct validity, were collected from 1055 adults with one or more chronic health conditions. Based on a validated conceptual model, confirmatory factor analysis (CFA) and item response models (IRT) were used to select and scale potential items and assess the internal consistency and structural validity of the PRISM-CC. To further assess construct validity, hypothesis testing of known relationships was conducted using structural equation models. Results: Of 100 potential items, 36 (4–8 per domain) were selected, providing excellent fit to our hypothesized correlated factors model and demonstrating internal consistency and structural validity of the PRISM-CC. Hypothesized associations between PRISM-CC domains and other measures and variables were confirmed, providing further evidence of construct validity. Conclusion: The PRISM-CC overcomes limitations of assessment tools currently available to measure patient self-management of chronic health conditions. This study provides strong evidence for the internal consistency and construct validity of the PRISM-CC as an instrument to assess patient-reported difficulty in self-managing different aspects of daily life with one or more chronic conditions. Further research is needed to assess its measurement equivalence across patient attributes, ability to measure clinically important change, and utility to inform self-management support.

Yu-Ting Chen,1,* Chen-Pang Hou,1– 3,* Horng-Heng Juang,1,4 Yu-Hsiang Lin,1,2 Pei-Shan Yang,1,4 Phei-Lang Chang,1 Chien-lun Chen,1 Shu-Chuan Weng,3 Ke-Hung Tsui1,5 1Department of Urology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; 2Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan City, Taiwan; 3Health and Management, Yuanpei University of Medical Technology, Hsinchu, Taiwan; 4Department of Anatomy, School of Medicine, Chang Gung University, Tao-Yuan, Taipei, Taiwan; 5Department of Urology, Shuang Ho Hospital, TMU Research Center of Urology and Kidney, School of Medicine, College of Medical, Taipei Medical University, Taipei, Taiwan*These authors contributed equally to this workCorrespondence: Ke-Hung Tsui, Taiwan Innovative Medical Association, Department of Urology, Shuang Ho Hospital, College of Medical, Taipei Medical University, Taipei, Taiwan, Email t2130@s.tmu.edu.tw Shu-Chuan Weng, Department of Healthcare Management, Yuanpei University of Medical Technology, Hsinchu, Taipei, Taiwan, Email scweng303@mail.ypu.edu.twBackground and Purpose: In this study, we compared patient outcomes between the 120-W thulium laser (Vela™XL) prostate enucleation (ThuLEP) and bipolar transurethral enucleation of the prostate (B-TUEP) techniques.Methods: We excluded patients with concomitant prostate cancer and bladder cancer and prospectively analyzed patients with benign prostatic obstruction (BPO) who underwent ThuLEP and B-TUEP from October 2018 to January 2021 in our institution. Patients’ demographics, comorbidities, prostate volumes, prostate-specific antigen (PSA) levels, and International Prostate Symptoms Score (IPSS) were recorded. Perioperative outcomes including intraoperative blood loss, prostate resection percentage of the transition zone, postoperative pain score (numeric rating scale, NRS), complications, changes in postoperative uroflowmetry parameters, IPSS, and the ra

Despite issues related to dendrite formation, research on Li metal anodes has resurged because of their high energy density. In this study, graphene oxide (GO) layers are decorated onto Li metal anodes through a simple process of drop-casting and spray-coating. The self-assembly of GO is exploited to synthesize coatings having compact, mesoporous, and macroporous morphologies. The abilities of the GO coatings to suppress dendrite formation are compared through Li|Li symmetrical cell charging at a current density of 5 mA cm-2 for 2000 cycles-a particularly abusive test. The macroporous structure possesses the lowest impedance, whereas the compact structure excels in terms of stability. Moreover, GO exhibits a low nucleation overpotential and is transformed into reduced GO with enhanced conductivity during the operation of the cells; both factors synergistically mitigate the issue of dendrite formation. Li-S batteries incorporating the GO-decorated Li anodes exhibit an initial capacity of 850 mA h g-1 and maintain their stability for 800 cycles at a C-rate of 1 C (1675 mA h g-1), suggesting the applicability of GO in future rechargeable batteries.

Despite issues related to dendrite formation, research on Li metal anodes has resurged because of their high energy density. In this study, graphene oxide (GO) layers are decorated onto Li metal anodes through a simple process of drop-casting and spray-coating. The self-assembly of GO is exploited to synthesize coatings having compact, mesoporous, and macroporous morphologies. The abilities of the GO coatings to suppress dendrite formation are compared through Li|Li symmetrical cell charging at a current density of 5 mA cm-2 for 2000 cycles-a particularly abusive test. The macroporous structure possesses the lowest impedance, whereas the compact structure excels in terms of stability. Moreover, GO exhibits a low nucleation overpotential and is transformed into reduced GO with enhanced conductivity during the operation of the cells; both factors synergistically mitigate the issue of dendrite formation. Li-S batteries incorporating the GO-decorated Li anodes exhibit an initial capacity of 850 mA h g-1 and maintain their stability for 800 cycles at a C-rate of 1 C (1675 mA h g-1), suggesting the applicability of GO in future rechargeable batteries.

The development of silicon anodes to replace conventional graphite in efforts to increase energy densities of lithium-ion batteries has been largely impeded by poor interfacial stability against liquid electrolytes. Here, stable operation of 99.9 weight% micro-Si (uSi) anode is enabled by utilizing the interface passivating properties of sulfide based solid-electrolytes. Bulk to surface characterization, as well as quantification of interfacial components showed that such an approach eliminates continuous interfacial growth and irreversible lithium losses. In uSi || layered-oxide full cells, high current densities at room temperature (5 mA cm 2), wide operating temperature (-20{\deg}C to 80{\deg}C) and high loadings (>11 mAh cm-2) were demonstrated for both charge and discharge operations. The promising battery performance can be attributed to both the desirable interfacial property between uSi and sulfide electrolytes, as well as the unique chemo-mechanical behavior of the Li-Si alloys.

By using temperature-dependent neutron powder diffraction combined with maximum entropy method analysis, a previously unreported Li lattice site was discovered in the argyrodite Li6PS5Cl solid-state electrolyte. This new finding enables a more complete description of the Li diffusion model in argyrodites, providing structural guidance for designing novel high-conductivity solid-state electrolytes.

[Figure: see text].

The development of silicon anodes to replace conventional graphite in efforts to increase energy densities of lithium-ion batteries has been largely impeded by poor interfacial stability against liquid electrolytes. Here, stable operation of 99.9 weight% micro-Si (uSi) anode is enabled by utilizing the interface passivating properties of sulfide based solid-electrolytes. Bulk to surface characterization, as well as quantification of interfacial components showed that such an approach eliminates continuous interfacial growth and irreversible lithium losses. In uSi || layered-oxide full cells, high current densities at room temperature (5 mA cm 2), wide operating temperature (-20{\deg}C to 80{\deg}C) and high loadings (>11 mAh cm-2) were demonstrated for both charge and discharge operations. The promising battery performance can be attributed to both the desirable interfacial property between uSi and sulfide electrolytes, as well as the unique chemo-mechanical behavior of the Li-Si alloys.

By using temperature-dependent neutron powder diffraction combined with maximum entropy method analysis, a previously unreported Li lattice site was discovered in the argyrodite Li6PS5Cl solid-state electrolyte. This new finding enables a more complete description of the Li diffusion model in argyrodites, providing structural guidance for designing novel high-conductivity solid-state electrolytes.

The development of silicon anodes for lithium-ion batteries has been largely impeded by poor interfacial stability against liquid electrolytes. Here, we enabled the stable operation of a 99.9 weight % microsilicon anode by using the interface passivating properties of sulfide solid electrolytes. Bulk and surface characterization, and quantification of interfacial components, showed that such an approach eliminates continuous interfacial growth and irreversible lithium losses. Microsilicon full cells were assembled and found to achieve high areal current density, wide operating temperature range, and high areal loadings for the different cells. The promising performance can be attributed to both the desirable interfacial property between microsilicon and sulfide electrolytes and the distinctive chemomechanical behavior of the lithium-silicon alloy.

© 2019 American Physical Society. We create a one-dimensional strongly correlated quantum gas of Cs133 atoms with attractive interactions by direct laser cooling in 300 ms. After compressing and cooling the optically trapped atoms to the vibrational ground state along two tightly confined directions, the emergence of a non-Gaussian time-of-flight distribution along the third, weakly confined direction reveals that the system enters a quantum degenerate regime. We observe a reduction of two- A nd three-body spatial correlations and infer that the atoms are directly cooled into a highly correlated excited metastable state, known as a super-Tonks-Girardeau gas.

Yong-Ren Wang, Zhao-Sheng Li, Wei Huang, Hui-Qiang Yang, Bo Gao, Yu-Ting Chen Department of Radiology, Chinese Medical Hospital of Yiwu, Yiwu, 322000, People’s Republic of ChinaCorrespondence: Yong-Ren WangDepartment of Radiology, Chinese Medical Hospital of Yiwu, No. 266 Xuefeng West Road, Yiwu, 322000, People’s Republic of ChinaTel +86 579 85439171Fax +86 579 85527330Email wangry65398@163.comBackground: The aim of this study was to discuss the value of susceptibility-weighted imaging (SWI) in evaluating the ischemic penumbra of patients with acute cerebral ischemic stroke.Methods: Data were collected from 52 patients with acute cerebral ischemic stroke upon clinical diagnosis and routine examinations of magnetic resonance imaging (MRI), including SWI, diffusion-weighted imaging (DWI), and perfusion-weighted imaging (PWI) within 72 hours after onset in this retrospective study. The methods also included fusing the DWI and SWI images and calculating the volume of anomaly extension of DWI and PWI-MTT (mean transit time) using semi-automatic analysis software. The SWI-DWI and PWI-DWI mismatches were interpreted, and the statistical analysis was completed.Results: The two physicians found that the ischemic penumbra consistency is high throughout the SWI-DWI and PWI-DWI mismatches, without a significant difference (P > 0.05).Conclusion: SWI-DWI mismatch can prevent the injection of contrast agents and make an accurate diagnosis of acute stroke ischemic penumbra, which helps guide the selection of the clinical therapeutic plan.Keywords: ischemic penumbra, diffusion-weighted imaging, susceptibility-weighted imaging, perfusion-weighted imaging

Sulfide-based solid electrolytes are known to have narrow electrochemical windows which limit their practical use in all-solid-state batteries (ASSBs). Specifically, when paired with a high-voltage transition metal oxide (TMO) cathode, the electrolyte will typically undergo unwanted degradation via chemical reactions or electrochemical oxidation, especially upon charging to voltages beyond the electrochemical stability window of the electrolyte. To mitigate these undesired reactions, thin (<10 nm), conformal, ionically-conducting, and electronically-insulating oxide-based protective coating layers have been applied on the cathode, typically via a solution process. In this work, a lithium borate-based (LBO) coating, prepared instead with a dry coating process, was shown to have the same beneficial properties. As evidenced by electrochemical characterization, the developed LBO coating shows good cycling performance and even performs better than the LiNbO3 coating commonly used in the literature. This new solvent-free coating method can thus be used to fabricate longer-lasting ASSBs.

Sulfide-based solid electrolytes are known to have narrow electrochemical windows which limit their practical use in all-solid-state batteries (ASSBs). Specifically, when paired with a high-voltage transition metal oxide (TMO) cathode, the electrolyte will typically undergo unwanted degradation via chemical reactions or electrochemical oxidation, especially upon charging to voltages beyond the electrochemical stability window of the electrolyte. To mitigate these undesired reactions, thin (<10 nm), conformal, ionically-conducting, and electronically-insulating oxide-based protective coating layers have been applied on the cathode, typically via a solution process. In this work, a lithium borate-based (LBO) coating, prepared instead with a dry coating process, was shown to have the same beneficial properties. As evidenced by electrochemical characterization, the developed LBO coating shows good cycling performance and even performs better than the LiNbO3 coating commonly used in the literature. This new solvent-free coating method can thus be used to fabricate longer-lasting ASSBs.

Analog computing hardwares, such as Processing-in-memory (PIM) accelerators, have gradually received more attention for accelerating the neural network computations. However, PIM accelerators often suffer from intrinsic noise in the physical components, making it challenging for neural network models to achieve the same performance as on the digital hardware. Previous works in mitigating intrinsic noise assumed the knowledge of the noise model, and retraining the neural networks accordingly was required. In this paper, we propose a noise-agnostic method to achieve robust neural network performance against any noise setting. Our key observation is that the degradation of performance is due to the distribution shifts in network activations, which are caused by the noise. To properly track the shifts and calibrate the biased distributions, we propose a "noise-aware" batch normalization layer, which is able to align the distributions of the activations under variational noise inherent in the analog environments. Our method is simple, easy to implement, general to various noise settings, and does not need to retrain the models. We conduct experiments on several tasks in computer vision, including classification, object detection and semantic segmentation. The results demonstrate the effectiveness of our method, achieving robust performance under a wide range of noise settings, more reliable than existing methods. We believe that our simple yet general method can facilitate the adoption of analog computing devices for neural networks., Comment: 7 pages, 3 figure

Temperature scaling has been widely used as an effective approach to control the smoothness of a distribution, which helps the model performance in various tasks. Current practices to apply temperature scaling assume either a fixed, or a manually-crafted dynamically changing schedule. However, our studies indicate that the individual optimal trajectory for each class can change with the context. To this end, we propose contextual temperature, a generalized approach that learns an optimal temperature trajectory for each vocabulary over the context. Experimental results confirm that the proposed method significantly improves state-of-the-art language models, achieving a perplexity of 55.31 and 62.89 on the test set of Penn Treebank and WikiText-2, respectively. In-depth analyses show that the behaviour of the learned temperature schedules varies dramatically by vocabulary, and that the optimal schedules help in controlling the uncertainties. These evidences further justify the need for the proposed method and its advantages over fixed temperature schedules.

Label hierarchies widely exist in many vision-related problems, ranging from explicit label hierarchies existed in image classification to latent label hierarchies existed in semantic segmentation. Nevertheless, state-of-the-art methods often deploy cross-entropy loss that implicitly assumes class labels to be exclusive and thus independence from each other. Motivated by the fact that classes from the same parental category usually share certain similarity, we design a new training diagram called Hierarchical Complement Objective Training (HCOT) that leverages the information from label hierarchy. HCOT maximizes the probability of the ground truth class, and at the same time, neutralizes the probabilities of rest of the classes in a hierarchical fashion, making the model take advantage of the label hierarchy explicitly. The proposed HCOT is evaluated on both image classification and semantic segmentation tasks. Experimental results confirm that HCOT outperforms state-of-the-art models in CIFAR-100, ImageNet-2012, and PASCAL-Context. The study further demonstrates that HCOT can be applied on tasks with latent label hierarchies, which is a common characteristic in many machine learning tasks.

Adversarial robustness has emerged as an important topic in deep learning as carefully crafted attack samples can significantly disturb the performance of a model. Many recent methods have proposed to improve adversarial robustness by utilizing adversarial training or model distillation, which adds additional procedures to model training. In this paper, we propose a new training paradigm called Guided Complement Entropy (GCE) that is capable of achieving "adversarial defense for free," which involves no additional procedures in the process of improving adversarial robustness. In addition to maximizing model probabilities on the ground-truth class like cross-entropy, we neutralize its probabilities on the incorrect classes along with a "guided" term to balance between these two terms. We show in the experiments that our method achieves better model robustness with even better performance compared to the commonly used cross-entropy training objective. We also show that our method can be used orthogonal to adversarial training across well-known methods with noticeable robustness gain. To the best of our knowledge, our approach is the first one that improves model robustness without compromising performance., Comment: ICCV'19 Camera Ready

Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes. We argue that, in addition to the primary objective, training also using a complement objective that leverages information from the complement classes can be effective in improving model performance. This motivates us to study a new training paradigm that maximizes the likelihood of the groundtruth class while neutralizing the probabilities of the complement classes. We conduct extensive experiments on multiple tasks ranging from computer vision to natural language understanding. The experimental results confirm that, compared to the conventional training with just one primary objective, training also with the complement objective further improves the performance of the state-of-the-art models across all tasks. In addition to the accuracy improvement, we also show that models trained with both primary and complement objectives are more robust to single-step adversarial attacks., Comment: ICLR'19 Camera Ready

Energy efficiency is one of the key considerations for various systems, from handheld devices to servers in a data center. Application-specific accelerators can provide 10 - 1000X energy-efficiency improvement over general-purpose processors through customization and by exploiting the application parallelism. The design of memory system is the key to improve performance and energy efficiency for both accelerators and processors. However, even with customization and acceleration, the single-server computation power is still limited and cannot support need of large-scale data processing and analytics. Therefore, the second goal of this dissertation is to provide customization support in the in-memory cluster computing system for such big data applications. The first part of this dissertation investigates the design and optimizations of memory system. Our goal is to design a high-performance and energy-efficient memory system that supports both general-purpose processors and accelerator-rich architectures (ARAs). We proposed hybrid caches architecture and corresponding optimizations for processor caches. We also provide an optimal algorithm to synthesize the ARA memory system. In the second part of this dissertation, we focus on improving the performance of an important domain, DNA sequencing pipeline, which demands huge computation need together with big data characteristics. We adopt the in-memory cluster computing framework, Spark, to provide scalable speedup while providing hardware acceleration support in the cluster. With such system, we can reduce the time of sequence alignment process from tens of hours to 32 minutes.

Energy efficiency is one of the key considerations for various systems, from handheld devices to servers in a data center. Application-specific accelerators can provide 10 - 1000X energy-efficiency improvement over general-purpose processors through customization and by exploiting the application parallelism. The design of memory system is the key to improve performance and energy efficiency for both accelerators and processors. However, even with customization and acceleration, the single-server computation power is still limited and cannot support need of large-scale data processing and analytics. Therefore, the second goal of this dissertation is to provide customization support in the in-memory cluster computing system for such big data applications. The first part of this dissertation investigates the design and optimizations of memory system. Our goal is to design a high-performance and energy-efficient memory system that supports both general-purpose processors and accelerator-rich architectures (ARAs). We proposed hybrid caches architecture and corresponding optimizations for processor caches. We also provide an optimal algorithm to synthesize the ARA memory system. In the second part of this dissertation, we focus on improving the performance of an important domain, DNA sequencing pipeline, which demands huge computation need together with big data characteristics. We adopt the in-memory cluster computing framework, Spark, to provide scalable speedup while providing hardware acceleration support in the cluster. With such system, we can reduce the time of sequence alignment process from tens of hours to 32 minutes.

Despite many advances in deep-learning based semantic segmentation, performance drop due to distribution mismatch is often encountered in the real world. Recently, a few domain adaptation and active learning approaches have been proposed to mitigate the performance drop. However, very little attention has been made toward leveraging information in videos which are naturally captured in most camera systems. In this work, we propose to leverage "motion prior" in videos for improving human segmentation in a weakly-supervised active learning setting. By extracting motion information using optical flow in videos, we can extract candidate foreground motion segments (referred to as motion prior) potentially corresponding to human segments. We propose to learn a memory-network-based policy model to select strong candidate segments (referred to as strong motion prior) through reinforcement learning. The selected segments have high precision and are directly used to finetune the model. In a newly collected surveillance camera dataset and a publicly available UrbanStreet dataset, our proposed method improves the performance of human segmentation across multiple scenes and modalities (i.e., RGB to Infrared (IR)). Last but not least, our method is empirically complementary to existing domain adaptation approaches such that additional performance gain is achieved by combining our weakly-supervised active learning approach with domain adaptation approaches., Comment: ECCV2018

Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performance in many tasks such as image classification and language understanding. However, most existing works only optimize for model accuracy and largely ignore other important factors imposed by the underlying hardware and devices, such as latency and energy, when making inference. In this paper, we first introduce the problem of NAS and provide a survey on recent works. Then we deep dive into two recent advancements on extending NAS into multiple-objective frameworks: MONAS and DPP-Net. Both MONAS and DPP-Net are capable of optimizing accuracy and other objectives imposed by devices, searching for neural architectures that can be best deployed on a wide spectrum of devices: from embedded systems and mobile devices to workstations. Experimental results are poised to show that architectures found by MONAS and DPP-Net achieves Pareto optimality w.r.t the given objectives for various devices., Comment: ICCAD'18 Invited Paper

Program verifiers are not exempt from the bugs that affect nearly every piece of software. In addition, they often exhibit brittle behavior: their performance changes considerably with details of how the input program is expressed-details that should be irrelevant, such as the order of independent declarations. Such a lack of robustness frustrates users who have to spend considerable time figuring out a tool's idiosyncrasies before they can use it effectively. This paper introduces a technique to detect lack of robustness of program verifiers; the technique is lightweight and fully automated, as it is based on testing methods (such as mutation testing and metamorphic testing). The key idea is to generate many simple variants of a program that initially passes verification. All variants are, by construction, equivalent to the original program; thus, any variant that fails verification indicates lack of robustness in the verifier. We implemented our technique in a tool called "mugie", which operates on programs written in the popular Boogie language for verification-used as intermediate representation in numerous program verifiers. Experiments targeting 135 Boogie programs indicate that brittle behavior occurs fairly frequently (16 programs) and is not hard to trigger. Based on these results, the paper discusses the main sources of brittle behavior and suggests means of improving robustness.

Recent studies on neural architecture search have shown that automatically designed neural networks perform as good as expert-crafted architectures. While most existing works aim at finding architectures that optimize the prediction accuracy, these architectures may have complexity and is therefore not suitable being deployed on certain computing environment (e.g., with limited power budgets). We propose MONAS, a framework for Multi-Objective Neural Architectural Search that employs reward functions considering both prediction accuracy and other important objectives (e.g., power consumption) when searching for neural network architectures. Experimental results showed that, compared to the state-ofthe-arts, models found by MONAS achieve comparable or better classification accuracy on computer vision applications, while satisfying the additional objectives such as peak power.

Despite the recent success of deep-learning based semantic segmentation, deploying a pre-trained road scene segmenter to a city whose images are not presented in the training set would not achieve satisfactory performance due to dataset biases. Instead of collecting a large number of annotated images of each city of interest to train or refine the segmenter, we propose an unsupervised learning approach to adapt road scene segmenters across different cities. By utilizing Google Street View and its time-machine feature, we can collect unannotated images for each road scene at different times, so that the associated static-object priors can be extracted accordingly. By advancing a joint global and class-specific domain adversarial learning framework, adaptation of pre-trained segmenters to that city can be achieved without the need of any user annotation or interaction. We show that our method improves the performance of semantic segmentation in multiple cities across continents, while it performs favorably against state-of-the-art approaches requiring annotated training data., Comment: 13 pages, 10 figures

When will a server fail catastrophically in an industrial datacenter? Is it possible to forecast these failures so preventive actions can be taken to increase the reliability of a datacenter? To answer these questions, we have studied what are probably the largest, publicly available datacenter traces, containing more than 104 million events from 12,500 machines. Among these samples, we observe and categorize three types of machine failures, all of which are catastrophic and may lead to information loss, or even worse, reliability degradation of a datacenter. We further propose a two-stage framework-DC-Prophet-based on One-Class Support Vector Machine and Random Forest. DC-Prophet extracts surprising patterns and accurately predicts the next failure of a machine. Experimental results show that DC-Prophet achieves an AUC of 0.93 in predicting the next machine failure, and a F3-score of 0.88 (out of 1). On average, DC-Prophet outperforms other classical machine learning methods by 39.45% in F3-score., Comment: 13 pages, 5 figures, accepted by 2017 ECML PKDD

2017 American Chemical Society. Al-doped Li 7-x La 3 Zr 2 O 12 is found to be more ionically conductive following voltammetric treatment in an all-solid-state Li|Li 7-x La 3 Zr 2 O 12 |Li cell configuration. This result is consistent with electrical impedance spectroscopy measurements, which reveal that the activation energy for lithium diffusion is reduced from 0.32 to 0.26 eV following voltammetric treatment. The Li deposition-dissolution signal has been observed in the voltammograms, and neutron powder diffraction shows an increase in the lithium content of the Li 7-x La 3 Zr 2 O 12 . Furthermore, X-ray photoelectron spectroscopy indicates a local rearrangement of O, resulting in a reduction of defects following voltammetric treatment, with the enhanced conductivity attributable to both the reduction of defect oxygen and increased lithium content. This work, therefore, reveals such voltammetric treatment as a simple and inexpensive alternative to existing doping approaches to boost the electrochemical performance of Li 7-x La 3 Zr 2 O 12 . The findings can improve the future development of all-solid-state Li-ion batteries. On the other hand, our approach to understanding the conductivity enhancement via voltammetric treatment may provide a better alteration in the ionic conduction of solid electrolytes during solid-state battery operation. (Graph Presented).

2017 American Chemical Society. Al-doped Li 7-x La 3 Zr 2 O 12 is found to be more ionically conductive following voltammetric treatment in an all-solid-state Li|Li 7-x La 3 Zr 2 O 12 |Li cell configuration. This result is consistent with electrical impedance spectroscopy measurements, which reveal that the activation energy for lithium diffusion is reduced from 0.32 to 0.26 eV following voltammetric treatment. The Li deposition-dissolution signal has been observed in the voltammograms, and neutron powder diffraction shows an increase in the lithium content of the Li 7-x La 3 Zr 2 O 12 . Furthermore, X-ray photoelectron spectroscopy indicates a local rearrangement of O, resulting in a reduction of defects following voltammetric treatment, with the enhanced conductivity attributable to both the reduction of defect oxygen and increased lithium content. This work, therefore, reveals such voltammetric treatment as a simple and inexpensive alternative to existing doping approaches to boost the electrochemical performance of Li 7-x La 3 Zr 2 O 12 . The findings can improve the future development of all-solid-state Li-ion batteries. On the other hand, our approach to understanding the conductivity enhancement via voltammetric treatment may provide a better alteration in the ionic conduction of solid electrolytes during solid-state battery operation. (Graph Presented).

2017 American Chemical Society. Al-doped Li 7-x La 3 Zr 2 O 12 is found to be more ionically conductive following voltammetric treatment in an all-solid-state Li|Li 7-x La 3 Zr 2 O 12 |Li cell configuration. This result is consistent with electrical impedance spectroscopy measurements, which reveal that the activation energy for lithium diffusion is reduced from 0.32 to 0.26 eV following voltammetric treatment. The Li deposition-dissolution signal has been observed in the voltammograms, and neutron powder diffraction shows an increase in the lithium content of the Li 7-x La 3 Zr 2 O 12 . Furthermore, X-ray photoelectron spectroscopy indicates a local rearrangement of O, resulting in a reduction of defects following voltammetric treatment, with the enhanced conductivity attributable to both the reduction of defect oxygen and increased lithium content. This work, therefore, reveals such voltammetric treatment as a simple and inexpensive alternative to existing doping approaches to boost the electrochemical performance of Li 7-x La 3 Zr 2 O 12 . The findings can improve the future development of all-solid-state Li-ion batteries. On the other hand, our approach to understanding the conductivity enhancement via voltammetric treatment may provide a better alteration in the ionic conduction of solid electrolytes during solid-state battery operation. (Graph Presented).

Lung cancer is the leading cause of cancer-related death, and 87% of these deaths are directly attributable to smoking. Using three-dimensional cultures of primary human bronchial epithelial cells, we demonstrated that loss of adherens junction protein, epithelial cadherin, and the aberrant interaction of its adherens junction binding partner, p120-catenin (p120ctn), with the cytoplasmic tail of apical mucin-1 (MUC1-CT) represent initiating steps in the epithelial-to-mesenchymal transition. Smoke provoked the rapid nuclear entry of p120ctn in complex with MUC1-CT that was inhibited using the MUC1-CT inhibitory peptides, PMIP and GO-201. Nuclear entry of p120ctn promoted its interaction with transcriptional repressor kaiso and the rapid shuttling of kaiso to the cytoplasm. Nuclear exit of kaiso permitted the up-regulation of oncogenic transcription factors Fos/phospho-Ser32 Fos, FosB, Fra1/phospho-Ser265 Fra1, which was inhibited through suppression of p120ctn's nuclear export using leptomycin-B. These data indicated that smoke-induced nuclear-to-cytoplasmic translocation of kaiso depends on the nuclear import of p120ctn in complex with MUC1-CT and the nuclear export of kaiso in complex with p120ctn. The presence of MUC1-CT/p120ctn and p120ctn/kaiso complexes in lung squamous cell carcinoma and adenocarcinoma specimens from human patients confirms the clinical relevance of these events. Thus, enhancing kaiso's suppressor role of protumor genes by sequestering kaiso in the nucleus of a smoker's airway epithelium may represent a novel approach of treating lung cancer.

Compared to conventional general-purpose processors, accelerator-rich architectures (ARAs) can provide orders-of-magnitude performance and energy gains and are emerging as one of the most promising solutions in the age of dark silicon. However, many design issues related to the complex interaction between general-purpose cores, accelerators, customized on-chip interconnects, and memory systems remain unclear and difficult to evaluate. In this paper we design and implement the ARAPrototyper to enable rapid design space explorations for ARAs in real silicons and reduce the tedious prototyping efforts far down to manageable efforts. First, ARAPrototyper provides a reusable baseline prototype with a highly customizable memory system, including interconnect between accelerators and buffers, interconnect between buffers and last-level cache (LLC) or DRAM, coherency choice at LLC or DRAM, and address translation support. Second, ARAPrototyper provides a clean interface to quickly integrate users' own accelerators written in high-level synthesis (HLS) code. The whole design flow is highly automated to generate a prototype of ARA on an FPGA system-on-chip (SoC). Third, to quickly develop applications that run seamlessly on the ARA prototype, ARAPrototyper provides a system software stack, abstracts the accelerators as software libraries, and provides APIs for software developers. Our experimental results demonstrate that ARAPrototyper enables a wide range of design space explorations for ARAs at manageable prototyping efforts, which has 4,000X to 10,000X faster evaluation time than full-system simulations. We believe that ARAPrototyper can be an attractive alternative for ARA design and evaluation., Comment: 14 pages